First and Second Language Reading Difficulty Among Chinese–English Bilingual Children: The Prevalence and Influences From Demographic Characteristics
Bibliographic record
Abstract
Learning to read a second language (L2) can pose a great challenge for children who have already been struggling to read in their first language (L1). Moreover, it is not clear whether, to what extent, and under what circumstances L1 reading difficulty increases the risk of L2 reading difficulty. This study investigated Chinese (L1) and English (L2) reading skills in a large representative sample of 1,824 Chinese-English bilingual children in Grades 4 and 5 from both urban and rural schools in Beijing. We examined the prevalence of reading difficulty in Chinese only (poor Chinese readers, PC), English only (poor English readers, PE), and both Chinese and English (poor bilingual readers, PB) and calculated the co-occurrence, that is, the chances of becoming a poor reader in English given that the child was already a poor reader in Chinese. We then conducted a multinomial logistic regression analysis and compared the prevalence of PC, PE, and PB between children in Grade 4 versus Grade 5, in urban versus rural areas, and in boys versus girls. Results showed that compared to girls, boys demonstrated significantly higher risk of PC, PE, and PB. Meanwhile, compared to the 5th graders, the 4th graders demonstrated significantly higher risk of PC and PB. In addition, children enrolled in the urban schools were more likely to become better second language readers, thus leading to a concerning rural-urban gap in the prevalence of L2 reading difficulty. Finally, among these Chinese-English bilingual children, regardless of sex and school location, poor reading skill in Chinese significantly increased the risk of also being a poor English reader, with a considerable and stable co-occurrence of approximately 36%. In sum, this study suggests that despite striking differences between alphabetic and logographic writing systems, L1 reading difficulty still significantly increases the risk of L2 reading difficulty. This indicates the shared meta-linguistic skills in reading different writing systems and the importance of understanding the universality and the interdependent relationship of reading between different writing systems. Furthermore, the male disadvantage (in both L1 and L2) and the urban-rural gap (in L2) found in the prevalence of reading difficulty calls for special attention to disadvantaged populations in educational practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".